ProQSAR: A modular and reproducible framework for small-data QSAR modeling with fit-and-use models.

backgroundQuantitative structure-activity relationship (QSAR) models are central to computer-aided drug discovery and predictive toxicology, but practical adoption is often impeded by ad-hoc tooling, inconsistent validation protocols, and poor reproducibility.

objectiveWe introduce ProQSAR, a modular, reproducible workbench that formalizes end-to-end QSAR development while permitting independent use of each component.

methodsProQSAR composes interchangeable modules for standardization, feature generation, splitting (including scaffold- and cluster-aware splits), preprocessing, outlier handling, scaling, feature selection, model training and tuning, statistical comparison, conformal calibration, and applicability-domain assessment. The pipeline can run end-to-end to produce versioned artifact bundles (serialized models) and analyst-oriented reports suitable for deployment and audit.

resultsOn representative MoleculeNet benchmarks evaluated under Bemis-Murcko scaffold split, ProQSAR attains state-of-the-art descriptor-based performance: the lowest mean RMSE across the regression suite (ESOL, FreeSolv, Lipophilicity; mean RMSE AVAILABILITY: ProQSAR is released on PyPI, Conda, and Docker Hub; all releases embed full provenance (parameters, package versions, checksums) to ensure reproducibility. SCIENTIFIC CONTRIBUTION: ProQSAR (i) enforces best-practice, group-aware validation together with formal statistical comparisons across models, (ii) integrates calibrated uncertainty quantification (cross-conformal prediction) and applicability-domain diagnostics for interpretable, risk-aware predictions, and (iii) exposes both a composable developer API and a one-click pipeline that generates deployment-ready artifacts and human-readable reports, demonstrated on representative benchmarks.